{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "api_key_project = \"\" #MK's Key\n",
    "#api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 1  # Choices: [1]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '250'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_experts'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Tracking run with wandb version 0.15.11"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "output_type": "display_data"
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     "data": {
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       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_093528-w128ratc</code>"
      ],
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     "data": {
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       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/w128ratc' target=\"_blank\">finetune_chetGPT_hatespeech_experts</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/w128ratc' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/w128ratc</a>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/w128ratc?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
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       "<wandb.sdk.wandb_run.Run at 0x17cbd2b77c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_1__task_1_train_set_250 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_1__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_1__task_1_train_set_250 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_1__task_1_train_set_250\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 879.8832487309645, 869.0\n",
      "p5 / p95: 853.0, 920.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.286802030456853, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346674 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1733370 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 997\n",
      "mean / median: 880.884, 868.5\n",
      "p5 / p95: 853.0, 928.1\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.004, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~220221 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1101105 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 566895\n",
      "\n",
      "#### Estimated cost: 4.54 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-XpuIcUPIlKaQbpd9lm7M3PMF\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich-ipz-phd:ds-1-t-1-trn-250:B5qLKS6v has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 48/63: training loss=0.04, validation loss=0.11\n",
      "Step 49/63: training loss=0.06, validation loss=0.01\n",
      "Step 50/63: training loss=0.01, validation loss=0.13\n",
      "Step 51/63: training loss=0.02, validation loss=0.07\n",
      "Step 52/63: training loss=0.01, validation loss=0.12, full validation loss=0.09\n",
      "Step 53/63: training loss=0.04, validation loss=0.12\n",
      "Step 54/63: training loss=0.02, validation loss=0.15\n",
      "Step 55/63: training loss=0.02, validation loss=0.06\n",
      "Step 56/63: training loss=0.02, validation loss=0.09\n",
      "Step 57/63: training loss=0.01, validation loss=0.05\n",
      "Step 58/63: training loss=0.00, validation loss=0.03\n",
      "Step 59/63: training loss=0.01, validation loss=0.07\n",
      "Step 60/63: training loss=0.02, validation loss=0.13\n",
      "Step 61/63: training loss=0.03, validation loss=0.06\n",
      "Step 62/63: training loss=0.05, validation loss=0.14\n",
      "Step 63/63: training loss=0.01, validation loss=0.07, full validation loss=0.13\n",
      "Checkpoint created at step 39\n",
      "Checkpoint created at step 52\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [04:35<00:00,  1.43it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.8401826484018265,\n",
      "                 'precision': 0.8440366972477065,\n",
      "                 'recall': 0.8363636363636363,\n",
      "                 'support': 110},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.8833746898263027,\n",
      "                       'precision': 0.9888888888888889,\n",
      "                       'recall': 0.7982062780269058,\n",
      "                       'support': 223},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.6987951807228915,\n",
      "                  'precision': 0.5523809523809524,\n",
      "                  'recall': 0.9508196721311475,\n",
      "                  'support': 61},\n",
      " 'accuracy': 0.8324873096446701,\n",
      " 'macro avg': {'f1-score': 0.8074508396503403,\n",
      "               'precision': 0.7951021795058493,\n",
      "               'recall': 0.8617965288405632,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.8427389674608194,\n",
      "                  'precision': 0.8808667436921523,\n",
      "                  'recall': 0.8324873096446701,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_1_t_1_trn_250>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_1__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_1__task_1_train_set_250'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "024029c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [05:13<00:00,  1.58it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.8501742160278747,\n",
      "                 'precision': 0.8531468531468531,\n",
      "                 'recall': 0.8472222222222222,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.8836206896551725,\n",
      "                       'precision': 0.9855769230769231,\n",
      "                       'recall': 0.80078125,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.751054852320675,\n",
      "                  'precision': 0.6223776223776224,\n",
      "                  'recall': 0.9468085106382979,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.8421052631578947,\n",
      " 'macro avg': {'f1-score': 0.8282832526679075,\n",
      "               'precision': 0.8203671328671329,\n",
      "               'recall': 0.8649373276201734,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.8486460319390315,\n",
      "                  'precision': 0.8778630276605985,\n",
      "                  'recall': 0.8421052631578947,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_1_t_1_trn_250>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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